Rob Sanchez is CEO of Anteriad, a leading provider of B2B marketing solutions. Data-Driven. Tech-Enabled. Growth-Obsessed.
Many CEOs I speak with are thinking about how AI can be woven into their business processes to increase efficiency, reduce costs and gain strategic insight. Unlike many technological advances in the past, AI is available to everyone. Foundational AI models like Chat GPT can be accessed by any company and major tech providers are building AI into their platforms so that all of their customers can use it.
Because of this commoditization, competitive AI strategies are only as good as the data that drives the AI decision-making. Therefore, a competitive AI strategy requires a competitive data strategy. Data needs to be accurate, representative, recent, comprehensive and comply with privacy best practices. It also must be unique. While it takes work, CEOs who build differentiated, high-quality data assets can build a competitive moat that differentiates them from the competition.
To achieve this—and ensure that data is being used effectively across the company—CEOs need a top-down strategy with clear goals and KPIs. The work is a two-step process, starting with a foundation of data best practices. From there, the company can build the data assets that differentiate it and hopefully deliver exponential returns.
Shoring Up Data Best Practices
Many companies struggle with data quality issues (paywall), yet they are already leaping to employ AI-driven models and processes. This can be dangerous for many reasons. Companies with low-quality data could end up with AI outputs that are inaccurate and biased, which could send the company’s strategy in the wrong direction, frustrate customers or worse. Conversely, companies with high-quality data usually end up with AI outputs that can deliver incremental increases in efficiency, insight and performance.
Thus, it pays to educate the entire company about the value of prioritizing high-quality data and put data best practices into place. IBM has a checklist of actions every company should take to improve data governance, create data feedback loops, update data regularly and train employees. If you haven’t already, work to ensure you have the basic elements in place so you won’t be hamstrung as AI infiltrates more and more business processes.
Creating a culture of data excellence not only helps data analysts, but everyone in the company that’s considering using data. For example, marketing and sales require high-quality data at scale to understand customers through AI analysis or personalization and business intelligence needs comprehensive metrics across departments to measure contribution and lift of different activities using AI. As you work to educate teams, share how these practices can benefit each team in their work—this will help with adoption and compliance.
Building A Competitive Data Moat
With data best practices in place, your company is now ready to utilize AI. As a CEO, you can begin thinking about how to use the tool to create a competitive advantage.
Superior Data
One strategy is to aim for superior data. This can be done by gathering data that is 100% compliant and cleansing data regularly. With pristine data, companies can get better insights and better outcomes than their competition that may be less diligent.
CEOs can work with their teams to build out more complete data, data that’s more scaled and create data sets with the most recent data possible. Data can be obtained via first-party data collection and/or partnerships with specific data providers. The right approach depends on what the data will be used for. For example, a company with a lot of self-service customers who buy online frequently could use AI for better on-site personalization. This requires comprehensive recent data that can be collected from the website, as well as online data partners that provide recent intent data.
Highly Specific Data
Another approach is to consider the first-party data that can drive specific business practices and focus on collecting the exact insights needed to drive lift. A manufacturing company that sells to large construction companies may want to use predictive models to forecast future demand. They might determine that the more they understand about the very specific types of projects each individual client has, the smarter their AI-based forecasts will be, and in turn, collect more insight about their customers’ projects. This can not only help the company’s financial outlook but also inform sales, marketing and customer strategies that are more relevant than those of competitors.
Finding the right competitive data to deliver the highest performance gains is an ongoing process. You can help your company achieve continuous growth by encouraging a culture that focuses on creating a hypothesis, testing and improving—and accepting that some tests will fall flat while others will be great successes. AI is a complex process and data strategies need to be tested, updated and improved over time. I believe CEOs who embrace the journey will experience the greatest advantage.
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